Failure events occur during fleet operation and spare parts must be available to keep aircraft flying. To deal with these failure events and prevent Aircraft on Ground (AOG) events from happening, aircraft operators have to maintain a spare parts inventory. An inventory control program is implemented in order to fulfill the highest possible number of spare parts demand at the least possible cost.
There is a set of classical inventory control models described in literature that can be used to establish an inventory control policy. Most of these models define an inventory policy based on total cost minimization. Inventory total cost is commonly broken down into or including the following factors:
Ordering Cost: The ordering cost is composed by two components: the acquisition cost and the setup cost. The acquisition cost is the price of the acquired spare parts, and it is proportional to the lot size. The setup cost is a constant cost that represents the administrative cost of placing and processing a new order.
Holding Cost: The holding cost (also known as storage cost) is the cost of keeping a spare part in the inventory from the moment it is bought to the moment it is actually used. In some models, the holding cost is expressed as a fraction of the spare part price per year. Holding cost comprises all cost related to spare part storage such as capital cost, warehouse rental cost, insurance cost, etc.
Stockout Cost: The stockout cost is the cost related to system unavailability for not having a spare part on hand to immediately replace a failed component. It is usually proportional to the time the system stays out of operation waiting for the spare part. Intangible costs such as company reputation and costumer satisfaction can be included in the stockout cost, when good estimations for these costs can be made.
The [R, Q] model is an easy-to-implement, typical model for inventory control. See e.g., Kennedy et al, “An Overview of Recent Literature On Spare Parts Inventories”, Int'l Journal of Production Economics, 76:201-15 (2002); Sun et al, “Multi-echelon Inventory Optimal Model of Civil Aircraft Spare Parts,” Chinese Control and Decision Conference, 824-28 (2010), both incorporated by reference. In this model, the inventory is continuously monitored and whenever the effective stock level drops to R units, an order for Q more units is placed to replenish the inventory. The effective stock is the sum of spare parts in the warehouse and replenishments ordered but not yet received.
The performance of the [R, Q] model depends on the quality of future demands forecast. When the accuracy of future demands estimations increases, the safety inventory level needed to fulfill demands and meet service level requirements decreases and the performance of the inventory management is positively affected. In most applications, the historical demand distribution is used to forecast future demands. However, past demands may not provide a good information for future demands forecast, especially when demand behavior can vary over time.
One of the disadvantages of the classical [R, Q] model is that both the reorder point R and the lot size Q are fixed. In most applications, the use of the [R, Q] model leads to good average demands estimations. However, this model does not estimate demand fluctuations around the average.